class_docstr: Plugin for Moe Hybrid Parallel Training.
  Tensor parallel, pipeline parallel and data parallel(DDP/ZeRO) can be picked and combined in this plugin.
  The size of tp and pp should be passed in by user, then the size of dp is automatically calculated from dp_size = world_size / (tp_size * pp_size).
  
  Example:
      >>> from colossalai.booster import Booster
      >>> from colossalai.booster.plugin import HybridParallelPlugin
  
      >>> model, train_dataset, optimizer, criterion = ...
      >>> plugin =  HybridParallelPlugin(tp_size=2, pp_size=2)
  
      >>> train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=8)
      >>> booster = Booster(plugin=plugin)
      >>> model, optimizer, criterion, train_dataloader, _ = booster.boost(model, optimizer, criterion, train_dataloader)
  
  Args:
      tp_size (int): The size of tensor parallelism. Tensor parallelism will not be used when tp_size is set to 1.
      pp_size (int): The number of pipeline stages in pipeline parallelism. Pipeline parallelism will not be used when pp_size is set to 1.
      precision (str, optional): Specifies the precision of parameters during training.
                                  Auto-mixied precision will be used when this argument is set to 'fp16' or 'bf16', otherwise model is trained with 'fp32'.
                                  Defaults to 'fp16'.
      zero_stage (int, optional): The stage of ZeRO for data parallelism. Can only be choosed from [0, 1, 2].
                                      When set to 0, ZeRO will not be used. Defaults to 0.
      enable_all_optimization (bool, optional): Whether to switch on all the optimizations supported by Shardformer.
                                                  Currently all the optimization methods include fused normalization, flash attention and JIT.
                                                  Defaults to False.
      enable_fused_normalization (bool, optional): Whether to switch on fused normalization in Shardformer. Defaults to False.
      enable_flash_attention (bool, optional): Whether to switch on flash attention in Shardformer. Defaults to False.
      enable_jit_fused (bool, optional): Whether to switch on JIT in Shardformer. Default to False.
      enable_sequence_parallelism (bool): Whether to turn on sequence parallelism in Shardformer. Defaults to False.
      enable_sequence_overlap (bool): Whether to turn on sequence overlap in Shardformer. Defaults to False.
      num_microbatches (int, optional): Number of microbatches when using pipeline parallelism. Defaults to None.
      microbatch_size (int, optional): Microbatch size when using pipeline parallelism.
          Either ``num_microbatches`` or ``microbatch_size`` should be provided if using pipeline.
          If ``num_microbatches`` is provided, this will be ignored. Defaults to None.
      initial_scale (float, optional): The initial loss scale of AMP. Defaults to 2**16.
      min_scale (float, optional): The minimum loss scale of AMP. Defaults to 1.
      growth_factor (float, optional): The multiplication factor for increasing loss scale when using AMP. Defaults to 2.
      backoff_factor (float, optional): The multiplication factor for decreasing loss scale when using AMP. Defaults to 0.5.
      growth_interval (int, optional): The number of steps to increase loss scale when no overflow occurs when using AMP. Defaults to 1000.
      hysteresis (int, optional):  The number of overflows before decreasing loss scale when using AMP. Defaults to 2.
      max_scale (float, optional): The maximum loss scale of AMP. Defaults to 2**32.
      max_norm (float, optional): Maximum norm for gradient clipping. Defaults to 0.
      broadcast_buffers (bool, optional): Whether to broadcast buffers in the beginning of training when using DDP. Defaults to True.
      ddp_bucket_cap_mb (int, optional): The bucket size in MB when using DDP. Defaults to 25.
      find_unused_parameters (bool, optional): Whether to find unused parameters when using DDP. Defaults to False.
      check_reduction (bool, optional): Whether to check reduction when using DDP. Defaults to False.
      gradient_as_bucket_view (bool, optional): Whether to use gradient as bucket view when using DDP. Defaults to False.
      static_graph (bool, optional): Whether to use static graph when using DDP. Defaults to False.
      zero_bucket_size_in_m (int, optional): Gradient reduce bucket size in million elements when using ZeRO. Defaults to 12.
      cpu_offload (bool, optional): Whether to open cpu_offload when using ZeRO. Defaults to False.
      communication_dtype (torch.dtype, optional): Communication dtype when using ZeRO. If not specified, the dtype of param will be used. Defaults to None.
      overlap_communication (bool, optional): Whether to overlap communication and computation when using ZeRO. Defaults to True.
code: def __init__(
      self,
      tp_size: int,
      pp_size: int,
      extra_dp_size: int = 1,
      precision: str = "fp16",
      zero_stage: int = 0,
      enable_all_optimization: bool = False,
      enable_fused_normalization: bool = False,
      enable_flash_attention: bool = False,
      enable_jit_fused: bool = False,
      enable_sequence_parallelism: bool = False,
      enable_sequence_overlap: bool = False,
      num_microbatches: Optional[int] = None,
      microbatch_size: Optional[int] = None,
      initial_scale: float = 2**16,
      min_scale: float = 1,
      growth_factor: float = 2,
      backoff_factor: float = 0.5,
      growth_interval: int = 1000,
      hysteresis: int = 2,
      max_scale: float = 2**32,
      max_norm: float = 0,
      broadcast_buffers: bool = True,
      ddp_bucket_cap_mb: int = 25,
      find_unused_parameters: bool = False,
      check_reduction: bool = False,
      gradient_as_bucket_view: bool = False,
      static_graph: bool = False,
      zero_bucket_size_in_m: int = 12,
      cpu_offload: bool = False,
      communication_dtype: Optional[torch.dtype] = None,
      overlap_communication: bool = True,
      use_ep_inside: bool = True,
      custom_policy: Policy = None,
  ) -> None:
      assert (
          dist.get_world_size() % (tp_size * pp_size) == 0
      ), f"world size {dist.get_world_size()} is not divisible by tp_size {tp_size} * pp_size {pp_size}"
  
      if enable_sequence_parallelism:
          assert (
              tp_size > 1
          ), "Sequence parallelism must be enabled when using tensor parallelism"
  
      self.tp_size = tp_size
      self.pp_size = pp_size
      self.dp_size = dist.get_world_size() // (tp_size * pp_size)
      self.precision = precision
      self.zero_stage = zero_stage
      self.cpu_offload = cpu_offload
      self.enable_all_optimization = enable_all_optimization
      self.enable_fused_normalization = enable_fused_normalization
      self.enable_flash_attention = enable_flash_attention
      self.enable_jit_fused = enable_jit_fused
      self.enable_sequence_parallelism = enable_sequence_parallelism
      # we change pg mesh to (pp, dp, tp) for better moe performance
      self.pg_mesh = ProcessGroupMesh(
          self.pp_size, self.dp_size, self.tp_size
      )
  
      # sync moe in outer dp group, and sync other param in global dp group
      if extra_dp_size > 1:
          ep_size = self.dp_size // extra_dp_size
          if use_ep_inside:
              self.pg_mesh_moe = ProcessGroupMesh(
                  self.pp_size, extra_dp_size, ep_size
              )
              self.moe_extra_dp_group = (
                  self.pg_mesh_moe.get_group_along_axis(1)
              )
              if dist.get_rank() == 0:
                  print(
                      f"Zero Parallel: pp {self.pp_size}, outer_dp {extra_dp_size}, inner_dp {ep_size}"
                  )
          else:
              self.pg_mesh_moe = ProcessGroupMesh(
                  self.pp_size, ep_size, extra_dp_size
              )
              self.moe_extra_dp_group = (
                  self.pg_mesh_moe.get_group_along_axis(2)
              )
              if dist.get_rank() == 0:
                  print(
                      f"Zero Parallel: pp {self.pp_size}, outer_dp {ep_size}, inner_dp {extra_dp_size}"
                  )
      else:
          self.moe_extra_dp_group = None
  
      self.stage_manager = None
      self.schedule = None
      self.custom_policy = custom_policy
      assert zero_stage in (0, 1, 2)
      if self.pp_size > 1:
          assert (
              num_microbatches is not None
              or microbatch_size is not None
          ), "num_microbatches or microbatch_size must be specified when using pipeline parallelism"
          assert (
              self.zero_stage <= 1
          ), "zero stage must be 0 or 1 when using pipeline parallelism"
          self.stage_manager = PipelineStageManager(
              self.pg_mesh, PP_AXIS
          )
          self.schedule = OneForwardOneBackwardSchedule(
              self.stage_manager,
              num_microbatches=num_microbatches,
              microbatch_size=microbatch_size,
          )
      self.tp_group = self.pg_mesh.get_group_along_axis(TP_AXIS)
      self.dp_group = self.pg_mesh.get_group_along_axis(DP_AXIS)
      self.pp_group = self.pg_mesh.get_group_along_axis(PP_AXIS)
      self.shard_config = ShardConfig(
          tensor_parallel_process_group=self.tp_group,
          pipeline_stage_manager=self.stage_manager,
          enable_tensor_parallelism=self.tp_size > 1,
          enable_all_optimization=self.enable_all_optimization,
          enable_fused_normalization=self.enable_fused_normalization,
          enable_flash_attention=self.enable_flash_attention,
          enable_jit_fused=self.enable_jit_fused,
          enable_sequence_parallelism=enable_sequence_parallelism,
          enable_sequence_overlap=enable_sequence_overlap,
      )
      self.amp_config = dict(
          initial_scale=initial_scale,
          growth_factor=growth_factor,
          backoff_factor=backoff_factor,
          growth_interval=growth_interval,
          hysteresis=hysteresis,
          min_scale=min_scale,
          max_scale=max_scale,
      )
  
      self.ddp_config = dict(
          broadcast_buffers=broadcast_buffers,
          bucket_cap_mb=ddp_bucket_cap_mb,
          find_unused_parameters=find_unused_parameters,
          check_reduction=check_reduction,
          gradient_as_bucket_view=gradient_as_bucket_view,
          static_graph=static_graph,
      )
  
      self.zero_config = dict(
          reduce_bucket_size=zero_bucket_size_in_m * 1024 * 1024,
          communication_dtype=communication_dtype,
          overlap_communication=overlap_communication,
          cpu_offload=cpu_offload,
          partition_grad=(self.zero_stage == 2),
      )
  
      self.max_norm = max_norm
  
imports: import random
  from types import MethodType
  from typing import Callable, Optional, OrderedDict, Tuple
  import numpy
  import torch
  import torch.distributed
  from torch.distributed import ProcessGroup
  from torch.nn import Module
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  from colossalai.booster.plugin.hybrid_parallel_plugin import HybridParallelAMPOptimizer, HybridParallelModule, HybridParallelNaiveOptimizer, HybridParallelPlugin, get_param_info, init_pipeline_optimizer
  from colossalai.cluster import ProcessGroupMesh
  from colossalai.interface import ModelWrapper, OptimizerWrapper
  from colossalai.moe import MoECheckpintIO
  from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule
  from colossalai.pipeline.stage_manager import PipelineStageManager
  from colossalai.shardformer import ShardConfig
  from colossalai.shardformer.policies.base_policy import Policy
  from colossalai.zero.low_level import LowLevelZeroOptimizer
label_desc: Use image node_id 1 to create a new MoeHybridParallelPlugin object from inherited base classes: HybridParallelPlugin with example: obj = MoeHybridParallelPlugin(tp_size, pp_size, extra_dp_size, precision, zero_stage, enable_all_optimization, enable_fused_normalization, enable_flash_attention, enable_jit_fused, enable_sequence_parallelism, enable_sequence_overlap, num_microbatches, microbatch_size, initial_scale, min_scale, growth_factor, backoff_factor, growth_interval, hysteresis, max_scale, max_norm, broadcast_buffers, ddp_bucket_cap_mb, find_unused_parameters, check_reduction, gradient_as_bucket_view, static_graph, zero_bucket_size_in_m, cpu_offload, communication_dtype, overlap_communication, use_ep_inside, custom_policy)